Predictive Models of Safety Belt Use: A Regression Analysis of MVOSS Data

Abstract

A substantial portion of the U.S. population fails to regularly use their safety belts. The explanations for the differential belt use have addressed, for example, socioeconomics, state law, attitudes, and perceived likelihood of being ticketed. The current analyses create predictive models of safety belt use. Using NHTSA's Motor Vehicle Occupant Safety Surveys (Years 1998 and 2000; N = 9577), variables related to belt use were entered into backward stepwise logistic regressions to produce two predictive models (Demographic and Attitudinal) of safety belt use (Always versus Not always). The results indicated that belt use is a complicated issue as there were several interactions between variables. The Demographic predictive model contained main effects for, law types, socioeconomics, population density, a gender–law type interaction, and a three-way interaction between age, marital status, and vehicle type. The Attitudinal model included perceived effectiveness of the belt, fatalistic attitudes, and an interaction between perceived effectiveness of the belt and perceived risk of being ticketed. These models survived a multinomial logistic regression when belt use was parsed into three categories (Always, Part-time, and Infrequent). In addition to variables that affect belt use, the results suggested that the structure of “belt use” as a psychological/ behavioral construct is more complicated than once thought. Specifically, a dichotomous breakdown of belt use (Always and Not always) oversimplifies the construct because the predictor factors sometimes affect “part-time” belt users differently than “infrequent” belt users (compared to “full-time” users). Many of the factors included in the models have been previously shown to impact belt use, but the interaction effects—indicating a more complicated relationship between these variables than previously suggested—may contribute to a better understanding of safety belt use.

Keywords:

• Safety Belts
• Demography
• Attitudes
• Regression Analysis